Colour Transfer By Feature Based Histogram Registration

A common problem in computer vision is that different sensors acquire different colour responses to an imaged object. This problem occurs because physical factors during the imaging process introduce a variation that differs for each sensor; in addition, it is practically impossible to image an object under perfectly constant lighting conditions at different spatial positions within an imaging environment. This variation degrades the performance of colour computer vision processes such as object tracking; in addition, the involved nature of calibration routines means that the calibration step is often ignored.

Author: Chris R. Senanayake
Author: Daniel C. Alexander

Publication: Proceedings of the British Machine Vision Conference 2007. Malvern, UK: British Machine Vision Association | full text (PDF)

Year: 2007

Fanning And Bending Sub-Voxel Structures In Diffusion MRI

We present a new model for fanning and bending white matter structures on a sub-voxel scale. We devise a parametric model of how the fibre orientation varies spatially over each sub-voxel in the voxel grid for both types of configuration. Fitting the model provides quantitative information about the degree of fanning or bending in each voxel. We demonstrate using data from a standard human brain diffusion MRI acquisition.

Author: Shahrum Nedjati-Gilani
Author: Daniel C. Alexander

Publication: International Society For Magnetic Resonance In Medicine (ISMRM) | full text (PDF)

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Year: 2009

Regularized Super-Resolution for Diffusion MRI

Diffusion MRI provides an insight into the microstructural architecture of tissue by observing the restricted and hindered displacement of water molecules undergoing Brownian motion in vivo. By looking at the probability density function p of displacements over a fixed period of time t, inferences can be made about the tissue microstructure.

Author: Geoff J. M. Parker
Author: Daniel C. Alexander
Author: Shahrum Nedjati-Gilani

Publication: IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI) ( pp.875-878). | full text (PDF)

Year: 2008